Render a 'photo-grid promo card' video from a config — a real-DOM card with a FIXED header/footer and a CONTINUOUSLY SCROLLING 2-row grid of MIXED tiles (video clips, product/lifestyle stills, big-serif %-OFF, dark promo-code or CTA) + feature chips, frame-stepped via Playwright with real-time <video> playback and encoded with FFmpeg — deterministic assembly, FREE (the clips + music come from create-video-fal + create-music-elevenlabs), text stays pixel-crisp. Use for the photo-grid-promo-card format.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill render-photo-grid-card
Render the 'photo-grid promo card' format from a config. The signature of this format is a
continuously scrolling 2-row grid (NOT a static card) under a fixed header (brand
wordmark + big headline + sub) and fixed feature chips. The grid-viewport scrolls left
across the whole ~10s, edge-faded with a CSS mask, and its tiles are mixed media:
video clips playing inside tiles, band-product/photo stills, big-serif pct/off
type tiles, and a dark code (promo code) or cta tile.
The renderer itself is FREE/deterministic (Playwright frame-step + FFmpeg). The paid inputs
are separate capabilities: the tile clips come from create-video-fal (i2v of the
product heroes, or clips extracted from the brand's own ad corpus) and the music from
create-music-elevenlabs. Text (wordmark, %, code) is real DOM — never AI-rendered.
build_card.py --config config.json --out hyperframe.html ; render.py --config config.json --html hyperframe.html --out master-silent.mp4 — 1080x1920, scrolling grid, deterministic, $0.
Clip tiles are driven by FRAME-SWAP, not <video>: render.py pre-extracts each clip to
PNG frames with ffmpeg, preloads them, and swaps each <img class="vidframe">'s src per
output frame. This is because Playwright's bundled Chromium can't decode <video> H.264
over file:// (open-source build, no proprietary codecs) — it hangs on canplay. Letting
ffmpeg decode makes it codec-independent AND deterministic. Provide clips longer than the
master (or the swap just loops them); a subtle setpts-slow reads as cinematic.
cols), NOT total tiles; the grid fills row-major.Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take gooseworks-ai/render-photo-grid-card from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.